AI Coding Agents Need Better Compiler Remarks

2026-04-15

AI Summary

This work addresses the limitations of existing compiler optimization feedback, which is often vague and unstructured, hindering effective utilization by AI coding agents and sometimes inducing semantic errors. To overcome this, the paper proposes a structured and precise compiler feedback mechanism that triggers reliable compiler transformations through targeted source code refactoring, preserving program semantics while enhancing maintainability and portability. Experimental evaluation on the TSVC benchmark demonstrates that, compared to ambiguous prompts, precise feedback improves the success rate of AI-driven optimizations by 3.3×, substantially reducing performance regressions and semantic inaccuracies. These results indicate that the primary bottleneck lies in the feedback interface rather than the AI agent itself, thereby laying a foundation for autonomous performance engineering.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Modern AI agents optimize programs by refactoring source code to trigger trusted compiler transformations. This preserves program semantics and reduces source code pollution, making the program easier to maintain and portable across architectures. However, this collaborative workflow is limited by legacy compiler interfaces, which obscure analysis behind unstructured, lossy optimization remarks that have been designed for human intuition rather than machine logic. Using the TSVC benchmark, we evaluate the efficacy of existing optimization feedback. We find that while precise remarks provide actionable feedback (3.3x success rate), ambiguous remarks are actively detrimental, triggering semantic-breaking hallucinations. By replacing ambiguous remarks with precise ones, we show that structured, precise analysis information unlocks the capabilities of small models, proving that the bottleneck is the interface, not the agent. We conclude that future compilers must expose structured, actionable feedback designed specifically for the future of autonomous performance engineering.

Problem

• AI coding agents
• compiler remarks
• optimization feedback
• structured interface
• autonomous performance engineering

Innovation

• AI coding agents
• compiler remarks
• structured feedback
• autonomous performance engineering
• program optimization